A metabolic engineering perspective which views recombinant protein
expression as a multistep pathway allows us to move beyond vector design and
identify the downstream rate limiting steps in expression. In E.coli these are typically at the translational level
and the supply of precursors in the form of energy, amino acids and nucleotides.
Further recombinant protein production triggers a global cellular stress response
which feedback inhibits both growth and product formation. Countering this requires
a system level analysis followed by a rational host cell engineering to sustain
expression for longer time periods. Another strategy to increase protein yields
could be to divert the metabolic flux away from biomass formation and towards
recombinant protein production. This would require a growth stoppage mechanism which
does not affect the metabolic activity of the cell or the transcriptional or
translational efficiencies. Finally cells have to be designed for efficient export
to prevent buildup of proteins inside the cytoplasm and also simplify downstream
processing. The rational and the high throughput strategies that can be used for the
construction of such improved host cell platforms for recombinant protein expression
is the focus of this review.

Host cell engineering has emerged as a powerful tool for designing
microbial platforms targeted at improved metabolite production. Major successes in
this area include improved production of isoprenoids, shikimic acid, isobutanol,
amino acids, synthesis of artemesin, lycopene and many such metabolites
[1-7]. The basic goal has been to redesign the complete pathway for
the biosynthesis of these metabolites by simultaneously engineering multiple steps
in the pathway. This has been achieved by a combination of many techniques such as
gene knock-ins and knock-outs, promoter engineering, supplementing the expression of
critical genes, enzyme engineering and modulation of the regulatory pathways. The
commonly used strategies to enhance the metabolite flux through a pathway can be
clubbed under the following categories a) Increase the flux through rate limiting
steps in the pathway; b) Increase the supply of precursors; c) Block branched chain
pathways which lead to by-product formation and d) Remove feedback controls in the
pathway (Figure 1).

Figure 1

Metabolic engineering strategies to enhance
the flux through a pathway. The various strategies
used to improve the flux from a cellular intermediate to the desired
product is shown. This includes enhancing pathways leading to the
formation of intermediates (shown in yellow) and pathways which are
rate limiting (B to C). Additionally branched chain pathways and
feed back controls need to be blocked.

It is possible to extend the same philosophy with minor modifications to
help in the design of hosts with improved recombinant protein expression capability.
Just like the pathways in metabolite synthesis, recombinant protein expression also
involves multiple steps viz. transcription, translation, folding and export. However
unlike a typical metabolic pathway these steps are intricately linked to the
cellular machinery with multiple host factors determining the flux through each step
of the pathway. Hence the cellular physiology and its dynamics have a critical role
in determining the overall flux through this pathway. Some important points that can
be flagged by this approach are summarized as follows; traditional genetic
engineering methods have mostly focused on improving the first step of this pathway
i.e. transcription and hence the gains from improved vector design have tended to
plateau over time. With strong promoters, the bottleneck in this pathway shifts to
the translational step which needs to be up regulated to match the rates of
transcription. Otherwise much of the gains of high rates of mRNA synthesis are
offset by higher rates of mRNA degradation [8-11]. The supply of
precursors which are critical to this pathway are the energy molecules like ATP,
amino acids and nucleotides which can become the rate limiting factors in protein
biosynthesis. Most importantly recombinant protein expression triggers a cellular
stress response which feedback inhibits both growth and product formation, by
lowering substrate uptake rates, down-regulating the ribosomal machinery and
biosynthesis of ATP (Figure 2). This has a
critical impact on the sustainability of the flux through this pathway and typically
specific product formation rates decline sharply within a few hours post induction.
Since host cell protein synthesis utilizes the same cellular machinery it can be
treated as a competing pathway. Thus one way to increase recombinant protein
synthesis would be to uncouple growth from product formation, thus allowing the
diversion of metabolic fluxes toward product formation. Finally an efficient export
mechanism needs to be in place, otherwise there is a theoretical upper limit to
which the recombinant protein can accumulate inside the cells. Moreover
extracellular expression would significantly simplify downstream processing steps.
The challenges associated with designing such host platforms using both rational as
well as high throughput strategies is the primary focus of this review.

Figure 2

Simplified schematic of the cellular stress
response on various factors affecting recombinant protein
synthesis. The down-regulated pathways are shown in
green (substrate uptake, ribosomes, translation rates, tRNA and ATP)
and up regulated pathways (Proteases, acetate formation and stress
response) are shown in red.

Improving transcriptional efficiency

The first step in the pathway for recombinant protein biosynthesis
has possibly received the largest attention in terms of improved vector design.
A very wide range of vectors are available both for E.coli and other microbes with specific features tailored for
different applications [12-14]. The rate
of mRNA synthesis is determined by both gene copy number and promoter strength,
however with strong promoters like the T7 and T5, plasmid copy number has a
relatively small role in expression. Rather the use of low copy number, stable
vectors allows for lowered levels of ‘leaky’ expression which is important while
expressing toxic proteins. Promoter design has thus focused more on titratable
and tightly regulated systems rather than strength alone [15-18]. Thus a slow and controlled expression which leads to a
properly folded protein can also be an important goal as is obtained with
titratable promoters using low inducer concentrations [19,20]. Additionally the use of fusion tags in vector constructs
can serve multiple purposes, like efficient purification, improved solubility,
increased mRNA stability and more efficient translation [21-27]. To further simplify the process of protein production
useful features like auto inducible systems [28-31] and
self-cleavable tags have been incorporated in vector design [32-34].

Removing translational bottlenecks

Translation has been identified as the rate controlling step in
recombinant protein synthesis for most high expression systems
(Figure 3). Many factors have a role
in controlling translational efficiency including, the first few codons of the
mRNA to be translated (translation initiation) and the mRNA secondary structure.
The ribosomal binding site (RBS) secondary structure is highly important for
efficient initiation of translation. Recombinant protein translation in
E.coli may be inhibited by presence of
secondary structures in the RBS as well as 5’UTR region. Computational tools
like ‘ExEnSo’ (Expression Enhancer Software) offer a platform where heterologous
gene sequences can be designed on the basis of highest free energy so as to
avoid translation inhibition due to mRNA secondary structures. The software also
creates a 5’ primer on the basis of the ‘optimized’ sequence which can be used
in PCR experiments to amplify the coding sequence of heterologous gene
[35]. Similarly a predictive
method for designing synthetic ribosome binding sites has been developed which
enables a rational control over the protein expression level. This work combines
a biophysical model of translation initiation with an optimization algorithm to
predict the sequence of a synthetic RBS sequence that provides a target
translation initiation rate [36].
Another work involving a random combinatorial DNA sequence library has revealed
that not only the SD sequence but the entire UTR sequence, seems to play an
important role in the translational process [37] implying that the rate of translation can also be
rate-limiting. Translation rate calculators have been designed to estimate
protein translation rates based on the sequence of the mRNA and have been shown
to give good estimates of the actual level of protein expression [38].

Figure 3

Cellular factors controlling the rate of
translation. These factors which directly effect
translation efficiency are a) mRNA RBS secondary structure
affecting the Translation Initiation Rates (TIR) and the UTR
sequences, b) Degradation of ribosomes due to substrate
non-availability and RNase over expression, c) Availability of
charged tRNA which depends on ATP supply and codon bias.
Additionally global regulator like FIS also control translation
and stability.

The increase in utilization of the protein synthetic machinery upon
induction leads to a degradation of the ribosomal machinery, as a feedback
stress response to over expression [39,40] that
ultimately leading to a loss in the protein synthesis capacity. This decreased
capacity of cells to synthesize proteins, as part of the stringent response,
highlights the major challenges regarding the sustainability of recombinant
protein production. It has been shown that whereas E.coli ribosomes are stable during exponential growth and in the
stationary phase, degradation occurs between the transition stages and is
independent from the triggering effect of the alarmone ppGpp(p) [41]. Degradation of stable RNA is also
associated with conditions of starvation. Thus, depletion of any one of a number
of nutrients including phosphate [42], nitrogen [43], carbon [44], or evenMg+2 [45] leads to a dramatic loss of RNA. RNase
expression is also triggered during the stress response and can contribute to
degradation of stable RNA [46].

Rate of translation may also slow down due to non-availability of
aminoacylated transfer RNA (tRNA). The availability of charged aminoacylated
transfer RNA further depends on codon composition of the transcript. The
rationale behind codon usage optimization is to modify the rare codons in the
target gene to mirror the codon usage of the host [47,48]. It is also known that the availability of tRNA varies
significantly under different growth and stress conditions, which facilitates
cellular adaptation to translational dynamics across the genome. Experimental
measurements of tRNA concentrations and their charged fractions under stressful
conditions have shown that tRNA availability can vary significantly between
conditions and over time [49,50]. A
computational workflow for estimating codon translation rates based on tRNA
availability has been developed. This could be particularly important when
considering the over expression of a recombinant protein, where a specific codon
composition might lead to the depletion of certain charged tRNA pools
[51] or under amino acid
limited growth conditions that have been shown to lead to specific charging
patterns [52,53]. This deficiency may lead to amino acid
mis-incorporation and/or truncation of the polypeptide, thus affecting the
heterologous protein expression levels and/or its activity [54]. OPTIMIZER, JCAT, Synthetic Gene
Designer, DNAWorks, GeneDesign, Codon optimizer, GeMS are some of the online
tools available to optimize codon usage (reviewed in [55]).

Likewise there are models like Ribosomal Flow Model (RFM) which
analyses translation process on the basis of its physical and dynamical nature
[56]. It considers the effect
of codon order on translation rates, the stochastic nature of the translation
process and the interactions between ribosomes while predicting the translation
elongation step. This approach gives more accurate predictions of translation
rates, protein abundance and ribosome densities in comparison to contemporary
approaches. Another interesting feature that might be useful for recombinant
protein expression and folding is the Translational pause at a rare codon. This
provides a time delay to enable independent and sequential folding of the
defined portions of the nascent polypeptide emerging from the ribosome
[57].

Additionally there are regulatory genes that control the rates of
ribosome biosynthesis. CsrA, is a posttranscriptional global regulator that
regulates mRNA stability and translation, which in turn is regulated by two
sRNAs csrB and csrC [58-60]. The
E.coli DNA binding protein Fis is a
transcriptional modulator involved in the regulation of many cellular processes,
including the activation of rRNA synthesis. High-level expression of fis in early, mid, or late log cultures has been
shown to result in growth phase and medium-specific variations in cell growth,
rRNA synthesis, and ribosome content [61].

Improving energy availability

The synthesis of recombinant proteins is energy intensive and
interferes with the host physiology [62]. The high energy demand during recombinant protein
production leads to an enhanced need for ATP generation at the cost of biomass
formation [63]. This increases
maintenance energy requirements which manifests itself as an increased metabolic
burden on the cells [64]. In order
to sustain this energy demand, cells take up alternative pathways like substrate
level phosphorylation leading to acetate formation by carbon overflow
metabolism. As a consequence of reduced biomass formation, excess NADPH might be
converted to NADH via the soluble transhydrogenase, filling the electron
transport chain for additional ATP generation. This hypothesis is supported by a
positive correlation between ATP production and productivity, while an inverse
correlation exists between biomass yield and productivity. Although the ATP
generation rate increases with increasing demand, the TCA cycle activity remains
constant, indicating a limited capacity of the TCA cycle to overcome the
postulated metabolic burden [65].

It has been shown that protein synthesis consumes approximately
two-thirds of the total energy produced by a rapidly growing E.coli cell [66]. Consequently, much effort has been focused on
understanding the mechanisms of ATP and GTP usage during protein synthesis. It
was thus observed that phosphoenolpyruvate carboxykinase (PCK) when expressed in
E.coli under glycolytic conditions helped
in increasing the intracellular ATP levels, leading to enhanced protein
production, of both the model proteins GFP (intracellular) and Alakaline
Phosphatase (extracellular) [67].
Polymerization of amino acids as well as aminoacyl-tRNA synthetase requires
large portion of ATP to mediate amino acid-charged tRNA synthesis [68]. It is known that the concentration of
aminoacylated-tRNA (charged tRNA) molecules is higher in rapidly growing
bacteria, and it has been postulated that the availability of the charged tRNA
is one of the check points that determines the rate of protein translation
[69].

Cofactor regeneration

Cofactors play an important role in generation of correctly folded,
stable and functional recombinant proteins [70]. Any imbalance in cofactor consumption and regeneration
can lead to a severe reduction of growth. Since these are the driving forces
behind most anabolic pathways as well as oxidative phosphorylation, it is
necessary to design strategies to enhance cofactor regeneration. OptSwap is a
computational method which predicts strain designs by identifying optimal
modifications of the cofactor binding specificities of oxidoreductase and
complementary reaction knockouts [71]. Another mathematical framework, cofactor modification
analysis (CMA), is a well-established constraints-based flux analysis method for
the systematic identification of suitable cofactor specificity engineering (CSE)
targets while exploring global metabolic effects [72]. Several genetic strategies employed for cofactor
engineering have been reviewed earlier [73-76].

Facilitating protein folding and export

A major effort in recent years has focused on improved protein
folding in vivo using chaperone co-expression
[40,77-79]. These molecular chaperones essentially belong to Hsp70
chaperone family. Thus DnaK which is an Hsp70 homolog binds to unfolded
hydrophobic stretches and helps protein folding while chaperones like GroEL
encapsulates the nascent polypeptide and prevents inter molecular interactions
[80]. A major issue is matching
the availability of chaperones with the rates of production of the nascent
polypeptide to prevent misfolding. This is a problem when high level expression
systems are used. The only way to circumvent this is to have lower but sustained
rates of protein expression leading to a slow buildup of the recombinant
protein. Another important aspect is providing an oxidizing environment for
correct disulfide bond formation in the cytoplasm or catalyzing bond formation
of the oxidized protein in the periplasm [81]. This has been attempted by introducing genes for
formation of disulfide bonds. Thus strains capable of producing properly folded
proteins, even those with multiple disulphide bonds, are now available
[82-87]. An E.coli strain has recently been designed for protein transport
which oxidizes disulfide bonds in the cytoplasm and then efficiently exports
these disulfide containing proteins using a signal peptide. These test proteins
include alkaline phosphatase (PhoA), a phytase containing four disulfide bonds
(AppA), an anti-interleukin 1bscFv and human growth hormone [88].

The more challenging task is protein export not just to the
periplasm but to the extracellular medium. This would not only greatly simplify
purification but also remove the upper bound on the accumulation of proteins in
the culture. There are five pathways for protein secretion in E.coli Type I, II, III, IV and V. However, only the
first and second secretion pathways are commonly used in recombinant protein
secretion. Type I pathway directly targets proteins from cytoplasm to
extracellular medium [89,90]. Studies
have shown that the Type II Sec dependent pathway gets overloaded leading to an
accumulation of unfolded proteins [91-95]. Plasmid
based over expression of SecY, SecE and SecG proteins, which are the major
interacting partners of SecA, resulted in a strong enhancement of a)
translocation ATPase activity, b) preprotein translocation, c) capacity for SecA
binding, and d) formation of the membrane-inserted form of SecA [96] (Figure 4). There are reports of a few proteins which get naturally
secreted into the medium [97-99]. Others
like GFP which do not get secreted through the Sec dependent pathway have been
successfully exported via a modified TAT dependent secretion pathway
[100]. In another work
synthetically designed lipase ABC transporter domains (LARDs) from P.fluorescens lipase were attached to GFP and
epidermal growth factor (EGF). The fused proteins were successfully secreted
with the ABC transporter and showed lipase activity as an intact fused form in
the supernatant [101]. These
examples highlight some of the important developments in this area which has the
potential of making E.coli into a truly
secretory protein expression system.

Figure 4

Various pathways for protein translocation
(Type I and II). The type II Sec-dependent
pathway is the most commonly used and gets overloaded leading to
accumation of mis folded proteins in the cytoplasm. However the
TAT dependent pathway is used for export of folded proteins and
thus requires proper folding in the cytoplasm
itself.

Feedback inhibition of product formation

It is well known that growth rates decline post induction in most
cultures. It was earlier postulated that this was due to the ‘metabolic burden’
associated with the diversion of metabolic fluxes towards recombinant protein
synthesis [102]. However a careful
analysis of experimental data shows that this rate of decline of growth post
induction, in the absence of substrate limitation is an intrinsic property of
the cell and specific to the protein being expressed [103]. Thus some proteins like γ –interferon
[104], α-interferon
[105] even when they are
expressed at high levels do not adversely affect growth while others like
insulin [106], GMCSF [107,108], streptokinase [109], lead to a complete growth stoppage post–induction.
Interestingly this retardation is also dependent on whether a protein is
expressed as inclusion bodies or as a soluble protein like GMCSF and
streptokinase [110-112]. Therefore attempts to explain growth
retardation in terms of the amino acid composition of the expressed protein has
limited predictive value [51,113,114] though
clearly different amino acids impose different metabolic requirements on the
cell. Clearly a static “diversion of fluxes” model is inadequate to explain this
growth retardation rather a complex cellular dynamics controls both the growth
and product formation kinetics.

This phenomenon of growth retardation is better understood by
analyzing the cellular stress response to recombinant protein expression, which
characteristically depends on the nature of protein, the form of expression
(whether soluble or inclusion bodies) and the level of expression (whether from
a strong or weak promoter) [115,116].
Moreover environmental factors such as the medium composition (presence or
absence of complex nitrogen sources) [19,84,117,118] and
the specific growth rate may also effect the nature of this response
[119,120]. Studies have shown that this stress
response mimics the features of the heat shock response, the oxidative stress
response and the stringent response [39,114,121]. There
are a set of common genes which get up or down-regulated due to this response
which belong to the substrate uptake, amino acid and ribosomal biosynthesis
genes and those involved in energy metabolism [39,121]. Others
which are specific to the form of expression, like IB expression are clpBP, dnaJK,
groLS, grpE, hslRUV, htpGX, ibpAB,
lon, rlmE, rpoD, yrfI [122-124]. It is
difficult to model this stress response using systems biology tools like Flux
Balance Analysis (FBA) to predict the changes in fluxes of various pathways
[125,126]. This is primarily because the
commonly used metabolic model of E.coli with
the largest set of pathways covers only ~30% of genes which are actually present
in the organism [127,128]. Most of the differentially expressed
genes are not part of this metabolic network and this unavailability prevents
their expression mapping and FBA analysis using the model. Thus the stress
response is better modeled as the triggering of key regulatory genes which in
turn trigger a cascade of other downstream genes [121]. Efforts have been made to develop
regulatory models which can analyze the complex interplay of the regulatory and
metabolic networks [129-132]. These
models could be applied in the analysis of the stress response due to
recombinant protein over expression and provide us with leads for designing
improved expression platforms. However there are as of now very few published
reports on attempting to modulate this stress response by gene knock-ins or
knock-outs. One would expect that knock-out of non-essential genes which get
up-regulated due to the stress response or conversely supplementing gene
expression of the down-regulated genes may help alleviate this stress and have a
beneficial effect on recombinant protein expression [133,134].

The use of metabolic engineering strategies to remove the
bottlenecks in recombinant protein production identified by analyzing this
stress response has helped in improving the supply of precursors like NADPH,
modification of global stress regulators and increasing the flux of the down
regulated metabolic pathways including that of substrate uptake [135-140]. Thus increasing glycerol uptake by over expressing the
glpK gene, lead to a 35% higher rhIFN-β
expression as compared to control cultures [133]. The issue of acetate formation has been solved by
knocking out genes (ackA, pta, ppc and
poxB) in the acetate biosynthesis pathway
leading to improve the recombinant protein production [135,138,140].
Supplementation of down regulated genes either through plasmid based expression
or chromosomal integration have been successfully tried, e.g. Expression of the
zwf gene coding for glucose-6-phopshate
dehydrogenase in the Pentose Phosphate pathway helps to provide building blocks
like nucleotides and NADPH and thus improves recombinant protein expression
[139]. Knock out of ppGpp as
well as the deletion of the global regulator rpoS (which is triggered by ppGpp) has been shown to enhance the
recombinant protein expression [141-144]. The
metabolic engineering strategies to improve the E.coli phenotype for recombinant protein production has also
been reviewed earlier [145].

Uncoupling growth from product formation

The growth associated nature of recombinant protein production
means that high specific growth rates need to be maintained in order to get high
specific product formation rates. Since product concentration in a bioreactor is
determined both by biomass concentration and specific product yield, we have the
twin requirements of growing cells to high cell densities while simultaneously
maintaining high specific growth rates. Together these requirements usually lead
to oxygen or heat transfer limitations in the bioreactor especially during scale
up. Hence the ability to produce product at high rates using slow growing or
non-growing cells could greatly simplify the bioprocess strategy for high level
product formation. Secondary metabolites are a very good example of how the
non-growth associated nature of product formation kinetics allows the easy
separation of growth and product formation phases in a bioreactor. In the case
of recombinant protein synthesis, we need to ensure that the resting cells are
metabolically active in terms of substrate uptake and energy metabolism as well
as transcription and translation.

One interesting development in this regard was the Quiescent cell
expression system where growth and product formation kinetics were decoupled
[146]. Growth stoppage was
achieved by over expressing a small RNA ‘Rcd’ which blocks cell division.
However recombinant protein expression is unimpaired, and since the
translational machinery is not required for biosynthesis, these cells have a
significantly higher productivity compared to normally growing cells
[147]. Further studies on the
mechanism of Rcd showed that it binds to tryptophanase leading to the
overproduction of indole [148].
Thus an exogenous supply of indole was also able to block cell division without
affecting recombinant protein expression. However indole targets multiple sites
in the cell [149-151] and may not be a preferred option for
recombinant protein expression. Therefore targets downstream of indole which
specifically blocks cell growth without affecting metabolic activity needs to be
identified in order to achieve improved quiescence.

Tools for host cell engineering

This section deals with the vast array of techniques that have now
become available, greatly simplifying the task of host modification to obtain
the desirable phenotype by rational or high throughput approaches.

Single gene modification strategies

While the use of plasmid based methods for supplementing gene
expression may be useful in ‘proof of principle’ studies, they have severe
limitations. There is an upper limit to the number of target genes that can
be supplemented; also the level of supplementation may be far higher than
desired, leading to an unnecessary metabolic burden on the cells. Thus
chromosomal engineering which leads to the construction of plasmid-less,
marker-less strains has the advantage of extending the practical
exploitation of the modified hosts in industry [152]. Also promoter engineering allows
us to fine tune the expression of genes to desired levels [153,154]. One of the earliest strategies to design
Single-gene knockouts was using the λ RED-ET system. Here the gene to be
knocked out is replaced with an antibiotic resistance gene, usually
kanamycin or chloramphenicol. If required, the selection marker can be
removed by expressing the Cre or FLP recombinases that acts on the FRT or
loxP site that is present in the flanking region of the selection marker or
antibiotic cassette [155-161].
Another commonly used method for Single-gene knockouts is the P1-mediated
transduction [162-166]. This method has gained popularity
because of the availability of the Keio library of single gene knock-outs of
non-essential genes in BW25113 which can be easily transferred into almost
any E.coli strain [167]. Many researches prefer to use the
vector plasmid pKO3 which integrates into the chromosome by homologous
recombination creating tandem duplication at the non-permissive temperature.
When shifted to the permissive temperature, the presence of the pSC101
replication origin in the vector ensures that it is excised from the
chromosome. The presence of the sacB gene
from B.subtilis in the vector allows us
to screen for the loss of the vector sequence by growing the cells in the
presence of sucrose [168].

The main limitation of these techniques is that they can be
applied for single gene modifications and if multiple knock-ins and
knock-outs have to be done, then these have to be done sequentially in a
time consuming manner.

High throughput genome engineering methods

High throughput methods have been developed for genome
engineering like Multiplex automated genome engineering (MAGE), Trackable
multiplex recombineering (TRMR) and use of small regulatory RNAs
[169-172]. These methods can create
simultaneous random combinatorial modifications in the E.coli genome. Till now these approaches have
mainly been used for evolutionary studies and pathway optimizations in
E.coli. The same strategy can be
applied for improving recombinant protein expression. As MAGE, works through
oligonucleotide-mediated allelic replacement in an iterative manner it is
capable of introducing multiple modifications in different locations of
genome. Therefore several oligomers can be designed to perform multiple
modifications iteratively, which can help in identifying the combinations
which lead to the desired phenotype including that of enhanced protein
capability (Figure 5). Recently, a
group led by Y.S. Ryu, has modified MAGE so that it is not restricted to
EcNR2 strains of E.coli [160]. This is important since it is
well known that there is a wide variation in the expression levels obtained
with different E.coli strains
[173]. Also strains
carrying different modifications and having the desired phenotype can be
combined in a step wise fashion using Conjugative assembly genome
engineering (CAGE) [174,175].
This approach can be used to look for synergy between various modifications.
The major drawback with MAGE is that it also accumulates unwanted off-target
mutations [175] and thus a
method for genome engineering at multiple locations with greater precision
needs to be developed [176].
Another method for rapid modification of many genes in E.coli is TRMR. This technique uses a large
number of synDNAs with multiple desirable sequence features, to modulate the
expression levels of genes. The synDNA contains different RBS which replaces
the native RBS and a Molecular barcode is used to track the allele in mixed
populations [177]. Another
novel approach for high throughput metabolic engineering is the use of a
transcriptional vector to express small chromosomal DNA fragments of
E.coli itself. Since some fragments
get inserted in the opposite orientation, they act as an anti-sense RNA and
create a library of down-regulated pathways which can be screened for
improved recombinant protein expression [178]. This has been extended to the use of synthetic
Small regulatory RNAs (sRNAs) which helps in the modulation of gene
expression [169]. This method
is useful when we need to down regulate gene expression rather than
completely knocking-out the gene, making it an indispensable tool for
studying the effect of essential genes on cellular phenotype.

Figure 5

Overall Strategy for host cell
engineering using rational and high throughput
methods. Leads obtained from analysis of
cellular physiology and Omics studies can be used to
identify system level bottlenecks. These can be addressed by
both single gene modifications or high throughput methods to
generate improved platforms for expression. These are
initially tested in microbioreactor format to select the
best clones for further scale-up.

High throughput screening strategies

The screening of a large number of gene knock-in and knock-outs
to select the desirable phenotype of improved expression capability is time
consuming. The simplest approach to screen a very large number of clones is
to use FACS based screening for cells expressing fluorescence tagged
proteins like GFP [179,180].
Thus libraries with engineered genomes can be screened for the highest
producers by using appropriate sorting protocols [181,182]. However such modified hosts may not necessarily
over express other proteins, given the very specific nature of host-protein
interactions. Another strategy would be the selection of quiescent
phenotype, in order to uncouple growth and product formation. For this one
can screen for a growth stoppage phenotype which typically leads to
elongated cell morphologies due to stoppage of cell division [183]. Simultaneously or later these
cells can be checked for recombinant protein expression capability after
growth arrest. Such techniques can be coupled with automated devices where
cultures can grow in 96 well plate formats. Such technologies have proven to
work well in clone screening and help in quickly identifying the best
performers from a large number of clones e.g. BioLector from m2p Labs
[184,185], Bioscreen C from Oy Growth Curves
Ab Ltd [186-190], Clone Screener from Biospectra AG
and the Ambr reactor from TAPBiosystems. Apart from growth profiling, these
systems can also do online monitoring of fluorescence, pH, dissolved oxygen
and NADH [185] and are
reviewed in [191-195].

The complex linkages between cellular physiology and the multiple steps
in recombinant protein synthesis makes the task of removing bottlenecks in this
pathway a difficult exercise. However we now have a wealth of information from
transcriptomic, proteomic and metabolomic studies on the cellular factors affecting
this pathway as well the changes in flux to this pathway due to the cellular stress
response. The data has been useful in rational design of host cells with better
expression capabilities. Also the use of high throughput screening methods have
allowed us to, reverse engineer these desired phenotypes, adding vastly to the
repertoire of beneficial knock-ins and knock-outs. With the development of tools for
genome scale engineering to generate multiple knock-ins and knock-outs we can now
study the synergistic response of these changes which were earlier limited to one or
two modifications. These could lead to major improvements in the design of host
platform for high level recombinant protein expression.

This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work is properly credited. The Creative
Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless
otherwise stated.